Deforestation, Growth and Agglomeration Effect

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Deforestation, Growth and Agglomeration Effects:

Evidence from Agriculture in the Brazilian Amazon

Danilo Camargo Igliori

Department of Land Economy, University of Cambridge

Departmento de Economia, FEA/USP

Endereo para Correspondncia:

Faculdade de Economia, Administrao e Contabilidade FEA USP

Cidade Universitria, Av Luciano Gualberto 908, Prdio FEA 2, 2o andar, Sala 1.

Cep: 05508-900, So Paulo, SP.

Email: [email protected]


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Deforestation, Growth and Agglomeration Effects:

Evidence from Agriculture in the Brazilian Amazon

Abstract

Population growth and migration have been emphasized as key variables explaining deforestation andland conversion in developing countries. The spatial distribution of human population and economicactivities is remarkably uneven. At any geographical scale we find that different forms of agglomerationsare pervasive. On the one hand, in central regions, agglomeration is reflected in a large variety of cities.On the other, less developed regions faces a dynamic process where new agglomerations form anddevelop as a result of frontier expansion. The recent literature on spatial economics has advertised therole of agglomeration and clustering of economic activities as fundamental causes of an enhanced level oflocal economic performance, as they host positive externalities that contribute with firms to grow fasterand larger than they otherwise would do. However, very little has been done to examine the presence of

agglomeration economies in regions where agricultural activities are predominant. In this paper weempirically examine whether initial levels of agglomeration impact the subsequent economic growth anddeforestation rates in the Brazilian Amazon. The regression estimates indicate that there is a significantnon-linear association between the initial intensity of agglomeration with both growth and landconversion in subsequent periods.

Resumo

Crescimento populacional e migrao tm sido enfatizados como variveis-chave na explicao dodesmatamento e processos de converso de ecossistemas em pases em desenvolvimento. A distribuio

espacial de populaes humanas e atividades econmicas bastante desigual. Em qualquer escalageogrfica observa-se diferentes formas de aglomerao. De um lado, em regies centrais, aglomeraesmanifestam-se em uma grande diversidade de cidades. Por outro lado, em regies perifricas, nota-se

processos dinmicos em que novas aglomeraes so formadas como decorrncia da expanso dasfronteiras de ocupao. A literatura recente de economia espacial tem advogado em favor do papel dasaglomeraes e concentraes de atividade econmica como causas fundamentais de uma maior

performance econmica local, uma vez que abrigam maiores externalidades que contribuem com ocrescimento das firmas. No entanto, muito pouco foi feito para examinar a presena de economias deaglomerao em locais com predominncia de atividades agrcolas. Neste artigo examinamosempiricamente se nveis iniciais de aglomerao impactam os subseqentes crescimento econmico etaxas de desmatamento na Amaznia Brasileira. As estimativas economtricas indicam que existe umasignificativa associao no-linear entre nveis iniciais de aglomerao e taxas de crescimento edesmatamento em perodos subseqentes.

Palavras Chave: crescimento econmico local, desmatamento, agricultura, anlise espacial, amaznia.

Keywords: local economic growth, deforestation, agriculture, spatial analysis, Brazilian Amazon

Cdigos JEL: Q10, Q24, R12

rea Anpec: Economia Agrcola e do Meio Ambiente


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1.Introduction

The role of population growth and migration has been emphasized as a key variable to explaindeforestation and land conversion in developing countries. In early studies a Malthusian process is putforward to associate the growing demand for resources caused by larger populations in frontier areas(Myers 1980, Walker 2004). Recent empirical research has also focused on the role of population

primarily as a measure for local demand and pressure over natural resources (Lugo et al 1981 , Allen andBarnes 1985 , Palo et al 1987, Rudel 1989, Cropper and Griffiths 1994, and Deacon 1994). The BrazilianAmazon is not an exception and population levels been part of all the empirical specifications in the samefashion (Reis and Guzman 1992, Reis and Margulis 1991, Pfaff 1999, Andersen and Reis 1997, Ferraz2001, Andersen et al 2002). However, in none of these studies population size is put into a moreanalytical context connecting the theory of land use with the modern developments of spatial economics.

The spatial distribution of human population and economic activities is remarkably uneven. Atany geographical scale we find that different forms of agglomerations are pervasive. At global level it iseasy to see that income and output is concentrated in a small number of industrialised countries.However, spatial concentration within countries is equally important. On the one hand, in central

countries or regions agglomeration is reflected in large varieties of cities as shown by the stability ofurban hierarchy within most countries (Fujita and Thisse 2002, p.2). On the other, less developed regionsfaces a dynamic process where new agglomerations form and develop as a result of frontier expansion.

The recent literature on spatial economics has emphasized the role of agglomeration andclustering of economic activities as fundamental causes of an enhanced level of local economic

performance, creating externalities that cause firms to grow faster and larger than they otherwise woulddo1. So far the theoretical and empirical work on these subjects have focused on urban contexts looking atthe existent relationships between firms and their capacity to generate positive externalities when in close

proximity2. However, very little has been done to examine the presence of agglomeration economies oneconomic performance of agricultural activities. Nevertheless, provided that agglomeration of economicactivity exist, in principle there is no reason to exclude less urbanised environments and more traditional

activities from the impacts suggested by the arguments put forward by modern spatial economics and anumber of cases are starting to become evident (Nadvi and Schmitz 1997, Schmitz 1999).

One important consideration in spatial economics is that the positive externalities generated byagglomerations could be offset to some degree by negative externalities due to congestion effects.Congestion is most likely in the densest agglomerations, so that it is an interesting empirical question toexamine whether the balance of positive and negative externalities swings in favour of congestion effectsat the higher levels of agglomeration. Again, congestion effects are typically associated with large urbanareas but in principle, when broadly defined, smaller towns and even rural areas could face some sort ofcongestion effects negatively impacting growth and economic performance. A second fundamental idealies on the relevance of transport costs for generating unequal patterns of distribution of economic

activity. Here proximity to markets for both inputs and outputs are central to explain growth anddevelopment of local areas.When looking to rural areas in developing countries, the counterparts of economic growth and

development are land use change and processes of deforestation. Absence of markets for biodiversity,ecosystem and climate stability, carbon repositories and environmental amenities have been listed asmain causes for generating conversion rates higher than the socially optimum. In addition, elements

1 The richness of agglomeration diversity is often encountered within an urban hierarchy. On the one hand we find largemetropolises, like New York, Tokyo, London or Paris, which are highly diversified. On the other, there are specialised townsand cities such as the so-called Italian districts and the Silicon Valley, or even factory towns like the Toyota City or the IBMsArmonk in New York. Agglomeration is also manifested in a very small spatial scale conforming the set up of inner cities.Agglomeration at this level take forms of commercial districts, like Soho in London, or of small agglomerations of theatres,

restaurants and shops in the same neighbourhood or even in the same street. Agglomerationing must also be embodied in ashopping mall (Fujita and Thisse 2002, p.2)2 See Fujita et al 1999, Fujita and Thisse 2002, Baldwin et al 2004 for surveys of the theoretical literature. See Thisse andHenderson 2003 for an account of the empirical research.


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responsible for boosting agricultural profitability are usually claimed as sources of deforestation.However, some level of deforestation would be expected anyway as a joint outcome of agriculturalactivities dependent on land as the main input. Accordingly, spatially specific characteristics such asaccess to markets, climate conditions and property rights structure represent the usual candidates forexplaining the variation of deforestation rates throughout the regions (Barbier and Burgess, 2001).Therefore the positive economic effects generated by agglomerations might also result in negative

outcomes in terms of environmental degradation. Thus, in order to understand whether agglomerationeconomies matter for rural areas in developing countries it is important to bring into the analysis thetrade-off conservation-development.

The Brazilian Amazon is perhaps one of the most interesting regions for analysing eventualrelationships between agglomeration economies, economic growth and deforestation. Firstly, the regionencompasses 5 million square kilometres of land of which roughly 85% is still forested areas (seeAndersen et al, 2002 for a brief description of the region). Secondly, agriculture, cattle ranching and othereconomic activities are unevenly distributed and rapidly expanding in many areas in the Amazonincreasing the pressure over forests. Thirdly, low levels of development and poverty are serious problemin the region, suggesting that the spatial distribution of economic activity matters in both efficiency andequity grounds.

In this paper we empirically examine whether an initial level of agglomeration impacts thesubsequent economic growth and deforestation rates in the Brazilian Amazon. We also test whethercongestion effects at the higher levels of agglomeration limit these impacts by a non-linear relationship.Apart from these externality effects, there are a number of other factors that should necessarily beincorporated into models of growth and deforestation. We therefore introduce some ancillary variables inan attempt to capture the spatial specific characteristics of local areas and provide a more comprehensiveexplanation of our data. To summarize the structure of the paper, after an initial synopsis of theoreticalissues, we then present the data used in the study and discuss the selection of variables. In the mainsection, spatial econometric models are estimated conditioning on the level of agglomeration and a set ofother initial conditions, thus capturing spillover effects across area boundaries. The regression estimates

indicate that there is a significant non-linear association between the initial intensity of agglomerationwith both growth and land conversion in subsequent periods. We also find evidence of other factorsassociated with growth and land conversion.

2. Drivers of Deforestation

Different models, estimation methods, data sources, and periods of analysis have been used to tryto identify the main factors driving deforestation in tropical areas (see Barbier and Burguess 2001, andKaimowitz and Angelsen 1998, and Brown and Pearce 1994 for extensive reviews). Among them there

are spatial regression models, which try to measure correlation between land use and other geo-referencedvariables. These variables include distance from markets and transportation infrastructure, topography,soil quality, precipitation, population density, forest fragmentation and zoning categories. In additionsocio-economic variables from census data have been incorporated to the models. Population is normallyincluded in the models generating a direct demand for land through subsistence activities or makingdeforestation more profitable by pushing down the wage rates. The typical results generated by regionalmodels suggest that landholders are most likely to convert forest to agricultural use where agriculture ismore profitable which normally is associated to good access to markets and favourable environmentalconditions for farming (Kaimowitz and Angelsen 1998).

The Brazilian Amazon is the focus of several of these studies (Pfaff 1999, Margulis, 2003, andAndersen et al 2002 are some of recent applications). We the remaining of this section we provide an

account of their main findings and approaches. Early empirical studies for the Brazilian Amazon (Reisand Margulis 1991 and Reis and Guzman 1992) find that population density, road density, and crop area


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to be important determinants of their deforestation measure. Deforestation patterns in the Amazon aretherefore primarily outcomes of economic decisions regarding alternative land uses.

The role of policy is discussed in many studies where road-building is alleged to be the mostimportant contribution to deforestation. Secondly, subsidised credits and other fiscal incentives had beencombined to increase the profitability of agricultural settlements. However, Andersen and Reis (1997)

present evidence showing that only about a third of the deforestation occurred between 1970 and 1985

was due to "aggressive" development policies. According to their results 9.6 million out of 33 millionhectares of deforestation can be attributed to road building or subsidised credit. Also, they suggest that72% of the policy-induced deforestation in this period was due to roads and 28% related to credits orsubsidies, indicating a much better trade-off in the second policy measure.

The dominant land use in the cleared land is pasture, accounting for more than three-quarters ofthe agricultural land. Chomitz and Thomas (2001) show that in average this land use presents very low

productivity and labour absorption, indicating that may not be a socially optimal use. An interestingfinding presented by these authors relates to the relationship between agricultural activities and levels ofrain precipitation. Their multivariate analysis shows that the probability that the land is currently claimed,used for agriculture or for cattle declines substantially with the precipitation level, holding other factorsconstant.

Inspired by an economic theoretical framework and merging satellite with census data, Pfaff(1999) has empirically estimated a model aiming to assess the drivers of deforestation in the region. Hefinds that a number of variables suggested by his land-use model are significant and helps to explain landconversion. Among them are some environmental characteristics such as soil quality and vegetation, andvariables impacting transport costs such as density of paved roads (in the county and in neighbouringcounties). Moreover, he finds that population density does not have a significant effect on deforestation.This is a surprising result and goes against most of the studies mentioned above. Nevertheless populationquadratic is significant and negative which he interprets as evidence that the first migrants have greaterimpacts than the later ones.

Adopting a data driven approach Andersen et al (2002) have estimated models for different

dependent variables including land clearing, rural and urban GDP, growth, rural and urban populationgrowth, and cattle herd growth. With a set up encompassing spatial and temporal factors the authors cometo the general conclusion that in addition to the spatial process of frontier maturation many processes inthe Amazon are now endogenously determined with growing centers of urban demand acting as a drivingforce behind many agricultural activities(p. 149). Moreover, their regression analsysis also provideevidence about the role of cattle ranching and transport network on deforestation dynamics.

An important result from their model relates to the relationship between roads and land clearing.They find that both paved and unpaved roads are associated with more clearing. However, their modelssuggest that the connections between roads and deforestation are more complex than usually assumed. Asthe impact of paved roads is stronger in areas that have been already cleared the authors argue that thedirection of causality is not clear but the models provide evidence that paved roads are associated withland intensive activities, typically part of the urban GDP, and unpaved roads associated to land extensiveactivities such as most of the agriculture carried out in the region.

It is clear from the studies mentioned above that in order to understand patterns of deforestation itis crucial to take into account local economic, social, demographic and environmental factors. This is

particularly acute in the case of the Brazilian Amazon because of its characteristics with respect to theagrarian structure. On the one hand, it is well known that land is extremely concentrated in the regionwith 1% of properties concentrating around 50% of the agricultural land. It is not clear whether smallestablishments with less than 20 hectares have similar production systems, choose same location or

pursue equal economic objectives of large farms with over 10,000 hectares. On the other, producers havedifferent conditions regarding land ownership. Owners, renters, sharecroppers and squatters carry out

agricultural activities in the region. They have different property rights and pay different prices for landuse (See Andersen et al 2002 for a description of land use rights in the region).


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Another issue that has been part of the political discussions regarding land use in the Amazon hasto do with farm size and deforestation. Fearnside (1993) argues that it is not clear that small landholdersare responsible for a large proportion of deforestation. However, since the policies providing theincentives for cattle ranchers to engage in large-scale land clearing have been scaled back or eliminated,the government claims that additional deforestation is primarily the work of small landholders. Fearnside(1993) concedes that small establishments do deforest land more intensively than large ones, however he

criticizes the governments position as being politically motivated as a way to characterizeenvironmentalists as being against the rights of poor people to improve their circumstances. He pointsout that some ranches still receive subsidies and many large establishments that continue to deforest landnever received government incentives to begin with.

In addition to theoretical considerations, Fearnside (1993) offers data to support his position thatin fact large landholders are still responsible for the lions share of deforestation in the Amazon. Hedivides agricultural establishments into three categories: small (less than 100 ha), medium (between 100and 1000 ha) and large establishments (over 1000 ha). Using data from the 1985 agricultural census andthe 1990 and 1991 estimates of deforested land from LANDSAT satellite data from INPE, he shows thatsmall farmers account for only 30% of the deforestation. His argument is largely descriptive: largeestablishments deforest more land in absolute terms than do small establishments because largeestablishments still occupy a much larger share of total land area than small establishments (62% versus11%). Because small landholders deforest land more intensively, if the proportion of smallestablishments were to increase then the rate of deforestation would be expected to increase accordingly.

Finally, cattle ranching has been singled out as the main source of deforestation and also ruralgrowth in the region. Margulis (2003) has shown that ranching activities are profitable in many parts ofthe Amazon especially in the southern part. Andersen et al (2002) has been also evidenced the role ofherd size. Using data from four specific sites in the Brazilian Amazon, Walker et al (2000) have estimatedmodels decomposing the pastureland in large and small ranching. They argue that large and small farmsdo not represent the same production systems as large farmers are attached to external capital and smallfarmers operates based on standard household choices. Their results support Fearnsides arguments and

suggest that large properties are accountable for a considerable share of deforestation in the region.However, they also argue that the contribution of small plots with cattle cannot be neglected. Moreovergiven the extremely high land concentration in the studied region the authors recognise that large farmsrelative contribution should be qualified. In addition, as the study relies on cross-sectional data anyattempt to establish causal relations is problematic.

Although most of the studies reviewed above adopt a regional approach focusing on spatiallyspecific characteristics as the main drivers of deforestation they do not attempt to link their findings to therecent developments in spatial economics, which focus on agglomeration effects. In addition with theexception of Walker et al (2000) the methods used do not take into account spatial econometric methodsto control for eventual spatial autocorrelation in their empirical estimations. Pfaff (1999) and Andersen etal make firs steps in that direction by including spatial lags of explanatory variables in their regressions

but do not look at the potential effects of spatial autocorrelation in the dependent variables and moregenerally in the disturbances. In the next section we provide the conceptual motivation for interpretingsome of the above results in the light of the literature on growth and agglomeration. Then after the

presentation of the data we pursue our empirical exercise estimating spatial econometric models.

3. Growth and Agglomeration

The observed spatial configuration of economic activities is the result of processes involving twoopposite types of forces: agglomeration (centripetal) and dispersion (centrifugal). These forces are

associated with increasing returns to scale, externalities, and imperfectly competitive markets. Afundamental idea of the literature on agglomeration is a shift in focus from the firm to productive systemsand an understanding of the phenomenon of competitiveness as a collective result rather than the outcome


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of individual processes. Increasing returns to scale are necessary to explain agglomerations primarily oneconomic grounds, without appealing to the attributes of physical geography. However, in case ofagricultural activities and rural environments the local physical geography also plays a crucial role.

The role of externalities is also fundamental to describe and understand the spatial concentrationof economic activities and population3. The idea is that cities, productive systems or agglomerations ofdifferent kinds are abundant in externalities (Fujita and Thisse 2002, Anas et al 1998, Fujita et al 1999,

Baldwin et al 2004, Duranton and Puga 2003, Porter 1998; Thisse and van Ypersele 1999). Of particularinterest is the role of communication externalities and face-to-face interaction in enhancing learning

processes and innovation.An early recognition of this phenomenon is of course to be found in the work of Alfred Marshall

(1920). For Marshall it was clear that specialisation as a result of an internal division of labour is one ofthe main drivers for an improvement in the efficiency and quality of the productive processes, and for thefirms growth (internal economies). However, these improvements could also be secured by geographicalconcentration of firms and external economies derived from integration among agents. Marshallidentified three main factors related to the external economies, which could stimulate industrialconcentration: the existence of thick markets for specialized labour, the occurrence of technologicalspillovers, and the emergence of subsidiary trades. The industrial concentrations would be sustainedwhile these external economies are strong enough to promote competitiveness.

Marshall was primarily concerned with externalities generated by firms within a particularindustry. However, Jacobs (1969, 1984) has suggested that the same arguments could be applied todiversified agglomerations where positives externalities would flow across sectors and contribute to their

productivity levels. The existence of such externalities could then explain why people are willing to payhigher rents to live in cities (see Glaeser et al 1992 for a detailed discussion).

More recently there have been several attempts to explain the existence of economicagglomerations through formal models, in which increasing returns in the firm's production function leadto pecuniary and technological external economies (Krugman 1991a, 1991b, 1995; Fujita and Thisse1996, 2002; Fujita et al (1999); and Baldwin et al 2004). This new literature has been labelled as the New

Economic Geography. The workhorse of the so-called New Economic Geography is the Core-PeripheryModel, which was first proposed by Krugman (1991a). Although recognising the value of the threesources of externalities originally proposed by Marshall, in the Core-Periphery Model Krugman adopts ahighly parsimonious set up focused on increasing returns, pecuniary externalities and transport costs4.

The mechanics of the model is driven by three effects: market access, cost of living, and marketcrowding. As summarised by Baldwin et al (2004), the market access effect describes the tendency ofmonopolistic firms to locate their production in the big market and export to small markets (an exogenouschange in the location of demand leads to a more than proportionalrelocation of industry to the enlargedregion); the cost of living effect concerns the impact of firms location on the local cost of living (goodstend to be cheaper in the region with more industrial firms since consumers in this region will import anarrower range of products and thus avoid more of the trade costs); the market crowding effect reflectsthe fact that imperfectly competitive firms have a tendency to locate in regions with relatively fewcompetitors.

The first two effects encourage spatial concentration while the third discourages it. Combining themarket-access effect and the cost-of-living effect with interregional migration creates the potential forcircular causality also known as cumulative causality, or backward and forward linkages. (Baldwinet al 2004, p.10 and 11). The basic result is that at some level of trade costs (break point) the

3 Following Scitovsky (1954) the concept of externalities is split in technological externalities (also called spillovers) andpecuniary externalities. The former deals with the effects of non-market interactions that are realised through processesdirectly affecting the utility of an individual or the production function of a firm. In contrast, pecuniary externalities are by-products of market interactions: they affect firms or consumers and workers only insofar as they are involved in exchanges

mediated by the price mechanism. Pecuniary externalities are relevant when markets are imperfectly competitive, for when andagents decision affects prices, it also affects well-being of others.4 For full presentation and several extensions of the Core-Periphery Model see Fujita et al 1999, Fujita and Thisse 2002 andBaldwin et al 2004.


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agglomeration forces overpower the dispersion force and self-reinforcing migration ends up shifting allindustry to one region (catastrophic agglomeration). On the other hand, when trade costs are very low andthe economy features catastrophic agglomeration, increases in trade costs will not change the geographyup to a threshold level where trade costs are high enough (sustain point) to generate dispersion forcesstronger than agglomeration forces, which motivate migration from the core to the periphery and generatea symmetric distribution of industry.

The typical New Economic Geography behavioural assumptions have been recently expanded toincorporate some alternative micro-foundations for agglomeration economies. Duranton and Puga (2003)distinguish three types of micro-foundations: sharing, matching and learning mechanisms5.

Micro-foundations of urban agglomeration economies based on sharing mechanisms mightinvolve sharing indivisible public facilities, sharing the gains from the wider variety of input suppliersthat can be sustained by a larger final-goods industry, sharing the gains from the narrower specializationthat can be sustained with larger production, and sharing risks. As for matching Duranton a Puga (2003)identify two sources of agglomeration economies: an increase in the number of agents trying to matchimproves the quality of each match, and stronger competition helps to save in fixed costs by making thenumber of firms increase less than proportionately with the labour force (p.19). The latter forceoriginates from the assumption that, as the workforce grows, the number of firms increases less than

proportionately due to greater labour market competition. As a result, each firm ends up hiring moreworkers, which in the presence of fixed production costs means higher output per worker. Also, in orderto examine the potential impacts of matching on income per worker it is possible to examine the issuelooking at mismatch costs.

Finally, when looking at learning Duranton and Puga (2003) discuss mechanisms based on thegeneration, the diffusion, and the accumulation of knowledge. In any of these mechanisms, learning it isnot a solitary activity. Instead it involves interactions with others and many of these interactions have aface-to-face nature (p.30). Since the original work by Jacobs (1969), numerous authors have beenstudying how cities contribute with the creation of new ideas. More importantly these authors haveemphasized that the advantages of cities for learning involve not only cutting edge technologies, but also

the acquisition of skills and everyday incremental knowledge.Knowledge accumulation has become the main aspect of learning processes due to its connections

with economic growth. As mentioned by Duranton and Puga (2003) there are two main approachesdealing with knowledge accumulation. The first one looks at the dynamic effects of static externalitiesand the second one focuses on dynamic externalities. In the former growth is driven only by theexternality in the city production function. In the latter approach, growth is driven by an externality in theaccumulation of human capital in the city. In both cases the externality plays a dual role as engine ofgrowth and agglomeration force.

The standard models in the New Economic Geography are only concerned with spatialdistribution of economic activity and dont take growth into consideration. However, those models have

been extended merging growth with geography through the combination of technological externalitieswith innovation and investment (for a discussion see Baldwin and Martin 2003 and Baldwin et al 2004).As stated by Baldwin and Martin (2003) growth and agglomeration are difficult to separate and the

positive correlation between them has been documented by economists working in different fields (Lucas1988, Williamson 1988, Fujita and Thisse 1996 and Quah 2002). For some agglomeration can be thoughtas the territorial counterpart of economic growth (Fujita and Thisse 2002).

Geography and growth models points to the existence of a possible spatial equity-efficiency trade-off. However, the matter is more subtle and perhaps more ambiguous than the standard win-lose situationresulting from the agglomeration process in static geography models. This kind of dynamic models ofgrowth and geography suggest that the emergence of regional imbalances, due to continual lowering of

5 The authors conclude that different microeconomic mechanisms may be used to justify the existence of cities. Moreover,these mechanisms generate final outcomes that are observationally equivalent in many respects. This point has an importantpolicy implication as it suggests that it might not be easy to identify which microeconomic mechanisms has been responsiblefor growth or decline of a particular city and therefore create problems for targeting policy initiatives.


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trade costs, is accompanied by faster growth in all regions and therefore generates a tension betweenstatic losses (relocation of economic activity) and dynamic gains (faster growth) in the periphery. Second,in some models, growth affects geography by creating a growth-linked circular causality; forces thatfoster the location of industry in a region also foster investment. Moreover, agglomeration process inthese models operates creating growth poles and growth sinks firms want to be in the growing region,

people want to invest in that region since it is growing and this investment in turn makes the region grow

faster (Baldwin and Martin 2003).With respect to knowledge spillovers Baldwin et al (2004) propose two different models: one with

global spillovers and another with local spillovers. In the global spillovers model growth can impactgeography as discussed above but geography is not relevant for growth because the transmission ofknowledge in innovation is unaffected by distance. Each region learns equally from an innovation madein any other region. This eliminates the importance of proximity and face-to-face interactions for thetransmission of knowledge. This model is interesting but of limited usage for our purposes here.Therefore we concentrate in the local spillovers model that assumes that some frictional barrier reducesthe diffusion of public knowledge to distant innovators and therefore re-establish the role of proximity inknowledge diffusion.

In the local spillovers model endogenous growth and knowledge accumulation is anagglomeration force (the region that head start in innovating finds that it accumulates innovationexperience faster than the other region. This lowers the replacement cost of capital faster which in turnattracts more resources to innovation in the fast-accumulating region). However, knowledge spillovers isa dispersion force (as spillovers become less localised the growth-linked agglomeration force becomesweaker). The combination of these two forces generate a new tension related to the integration of poorand rich regions. In other words in the local spillovers model integration can be stabilizing ordestabilizing whereas in the core-periphery model integration is always destabilizing (economicintegration eventually ends up creating extreme divergence between initially symmetric regions, i.e. thatintegration always fosters agglomeration). As Baldwin et al summarise a purely tradecost reducingintegration policy encourages agglomeration and eventually results in extreme agglomeration. By

contrast, a policy that lowers the cost of transporting both goods and public knowledge may avoidextreme agglomeration.Another important feature of the local spillovers model is that that economic geography can affect

the growth rate. Assuming a constant intensity of learning spillovers, the cost of innovation decreasessubstantially (innovation costs decreases with the size of the local economy) when the economy movefrom a symmetric to a core-periphery pattern. Therefore, by triggering agglomeration, trade integrationraises the economy to a higher growth path (growth take-off). As mentioned, the higher growth pathapplies to the whole economy not only to the core region due to the low cost innovation that spills overthe periphery. This crystallize the trade-off between static losses and dynamic gains where the netoutcome for the periphery is ambiguous.

4. Data and Variables

The empirical exercise covers the Brazilian Legal Amazon (AML), which is an administrativearea in the northern part of Brazil including 10 states and around 5million of km 2 (about 60% of theBrazilian national territory). The data used is part of a database (Desmat) managed by IPEA/DIMAC(The Directorate of Macroeconomic Studies of the Institute of Applied Economic Research, Brazil).IPEA/DIMAC assembled a data panel for all the municipalities of Brazilian Legal Amazon (AML)including thousands of variables on major economic, demographic and geo-ecological aspects. The unitof observation is the municipality (municpio), which compromises between the spatially detailed geo-

ecological information available in GIS and the systematic and relatively long time-consistent seriesavailable in socio-economic sources, in particular Demographic and Economic Census data observed in5-year periods from 1970 to 2000.


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To illustrate the relevance of this database for statistical analysis, it suffices to say that LegalAmazonia had 763 municipalities in 1997 (which were 508 in 1991). Another important aspect of thedatabase is to take account of changes in the number and areas of municipalities between Census years,thus providing information for a panel of comparable geographic areas from 1970 to 1997. For the period1970-1997 as a whole, the size of the panel is 257 comparable areas. In our analysis we use this 257comparable areas as geographical units using the Censuses of 1996 as the main source of information (for

a detailed presentation of this database see Andersen et al 2002).The dependent variables are growth rates of cleared land and output. Following Andersen et al

(2002) we choose growth rates rather than levels to avoid spurious correlations. The levels are highlytrending in several of the used variables. Moreover, by taking rates we eliminate some of themunicipality-specific fixed effects, which help to control for omitted variables. We use population size asour measure of agglomeration. As the municipalities vary considerably in terms of area size we include itas an additional exploratory variable. By doing that we are effectively measuring all the other variables interms of density. As mentioned above population size has been used in most studies as the key variable toexplain land use change. However, they have interpreted the impact of population purely in terms ofdemand effects. Here we extend the analysis and argue that population density can also capture theimpacts of positive and negative externalities generated by close proximity of agents. These effectsinclude the size of demand for agricultural outputs but also include thicker markets for labour andknowledge spillovers.

The remaining explanatory variables included in the models follow the literature and aim todescribe the local characteristics of municipalities. First, we include a number of spatial variables such asdistances to the state capital and Sao Paulo, and the length of roads. The latter is also meant to capture theimpact of public policies towards the provision of infrastructure. We also include a set of dummyvariables for states. Second, environmental characteristics are added including soil quality, rain

precipitation, average temperature, altitude, and length of rivers. Third, two cost variables are includednamely wages and land prices. As usual, wages are interpreted as a measure of labour productivity. Land

prices serve as an indication of proximity to the frontier. Fourth, we aim to capture the effect of human

capital by including a measure of local educational level. Fifth, in order to characterize the agrarianstructure we introduce a proxy for property rights (proportion of farms owned by the farmer) and theproportion of small properties (less than 50 hectares). Finally we also include the levels of both outputand cleared land to capture dynamic elements of growth and land use change. The measure of output onlyincludes agricultural products and the measure of cleared land is the total area of the municipality less thesum of different land uses (agricultural, pasture, fallow, planted forests, abandoned land). Table 3 presentthe descriptive statistics for all variables included in the models.

In order to test the impact of neighbourhood effects and to better characterize the spatial dynamicsin the region we also include spatially lagged variables for output, cleared land and population. Thespatial lags measure the averages values of those variables in surrounding areas. To spatially associate themunicipalities we construct a so-called Spatial Weight Matrix (W matrix henceforth), which is a squarematrix of dimension 257. The values in W reflect an ad-hoc hypothesis of spatial interaction between themunicipalities. The diagonal contains zeros, and the off-diagonal elements reflect the spatial proximity

between the municipalities. We follow fairly standard practice in assuming that interaction is adiminishing function of distance. For each municipality we set the distance decay for the 5 nearestneighbours and zero for the remaining ones. A further step in the construction of the W matrix is tostandardise it so that each row sums to 1. Hence

=

=

j

ij

ijij

ij

ij

WWW

dW

*

*

* 1


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10

Standardising helps with interpretation, since the value for area j of the spatial lag, defined as thej'th cell of Wx, is then the weighted average of the values of the variable x in the areas that are'neighbours' to J, and so its estimated coefficient can be compared directly to the coefficient forx. Also,using the standardised Wmatrix usefully identifies a parameter value below 1 as being consistent with a'non-exploding' process while 1 and above leads to complex and little understood consequences forinference and estimation (the mathematical background to this and implications of spatial unit roots

consistent with a parameter equal to 1 are discussed in Fingleton, 1999).

5. Empirical Model

In this section we set out a model that seeks to explain the change in local growth anddeforestation over the period 1985-1995. The model is a modified version of a growth model proposed byHenderson (2000). We envisage a non-linear relationship between agglomeration intensity and growthand deforestation, and this non-linearity reflects the presence not only of positive externalities but alsonegative externalities, with negative externalities becoming increasingly relevant as the agglomeration

intensifies, due to the effects of congestion6

. Hence, in the initial stages of increasing agglomerationintensity, it is likely that employment growth will increase as the externalities associated withagglomeration become more powerful. However, it is likely that some point negative externalitiesassociated with congestion will also start having an effect that will increasingly counteract the positiveexternalities as agglomeration intensity increases, to the point that employment growth will fall to zeroand then become negative. In order to test this hypothesis, we assume that change G (both in growth anddeforestation) is a quadratic function of agglomeration intensity and linear in a set of initial conditions, X,that also are assumed to determine the change of cleared land and output, hence our basic empiricalequation is

udcXbPaPG ++++= 8585

2

859685[1]

The model should have significant regression coefficients for both agglomeration intensity and thesquare of agglomeration intensity, with a positive coefficient on the former and a negative coefficient onthe latter. The hypothesis of increasing congestion effects is rejected if the coefficient on P2 is eitherinsignificantly different from zero or is positive.

In order to check for spatial autocorrelation and test the robustness of coefficients we extendequation 1 and estimate the following standard spatial econometric models (Anselin 1988, 2003). Ageneral homoskedastic spatial autoregressive model can be written as

eXWyy ++= , where e uWe += [2]

In this paper we consider the two usual particular cases. First the spatial lag model with 0= andsecond the spatial error model with 0= . These two models control for global spatial autocorrelationwhere neighbours at closer proximity carry more weight (Anselin 2003). Simple manipulation of spatiallag and spatial error models yields the respective following reduced forms

uWIXWIy 11 )()( += [3]

uWIXy 1)( += [4]

6 For a similar empirical application in an urban context see Fingleton et al (2005).


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In equation 3 we see that both explanatory variables and the disturbance are impacted by the samespatial multiplier ( in the spatial lag model. However, equation 4 shows that in the spatial error

model the spatial multiplier only operates in the autocorrelated disturbances. We extend thesetwo standard models to incorporate local spatial autocorrelation in three of the explanatory variableswhich the theory suggests that are likely to generate spatial spillovers (Anselin 2003, Florax and Folmer1992), namely output, population and cleared land. Thus the our complete versions for the spatial lag andspatial error models become

1) WI

( 1) WI

uZWXWyy +++= [5]

uWeZWXy +++= [6]

Where Z is a matrix including only output, population and cleared land variables.

Models depicted by equations 5 and 6 are initially estimated using the method of maximun likelihood proposed byAnselin (1988). This method requires that u follow a normal distribution. In order to test whether the assumption aboutnormality of residuals is significantly impacting the results we also estimate the spatial error model using the Generalised

Method of Moment estimator developed by Kelejian and Prucha (1998), which does not requires the normality assumption7.

6. Results

The four estimated models provide evidence of the determinants of both growth of agriculturaloutput and growth of cleared land. The estimated coefficients in the four models for each of ourdependent variables are robust as they are generally similar in value and significance. Tables 1 presentthe estimates for the 4 models of cleared land and Tables 2 present the estimates for the 4 models ofoutput.

As suggested by the literature on spatial economics the estimates for population, controlled forarea size, are significant for both the linear and quadratic terms, with positive coefficient for the linearvariable and negative for the quadratic one in the models for cleared land and output. These results

provide evidence that agglomeration intensity is relevant for the joint process of development and landcover change. Moreover the effects of agglomeration work in a similar way. For low levels ofagglomeration the increase in population size contributes to both economic growth and land conversion.However, at higher levels of agglomeration congestion effects start to quick in producing negativeexternalities that reduce growth and result in less land conversion as well. The results do not allow us toidentify what kinds of agglomerations effects are working in the case of the Brazilian Amazon anddistinguish the potential impacts of market size, public facilities sharing, better matching between firms

and workers or knowledge spillovers. Possibly we would find most of these factors in a greater or lesserextent depending on the local conditions.The results for levels of output and cleared land in 1985 allow us to extract some information

about the dynamic process of development and frontier expansion. In the cleared land regression, level ofcleared land has negative and significant coefficients and level of output have positive and significantcoefficients. These results suggest that the pace of deforestation tends to slow down as the areas becomemore cleared but at the same time additional output puts more pressure on deforestation regardless thelevel of land already cleared. However, in the output regression, output level is negative and significant

but the level of cleared land is positive but only marginally significant. This indicates an interestingrelationship between economic activity and land use. First we see that there have been some sort ofconvergence on growth and areas that have grown faster in the past are now slowing down. This has an

7 All models are estimated using Matlab codes adapted from James Le Sage spatial econometrics toolbox (see www.spatial-econometrics.com).


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impact over deforestation, as the growth of land clearing is associated with previous level of output.Second, as previous cleared land is contributing poorly to future growth and there are weak feed backsfrom deforestation to economic growth and therefore we could envisage that there are forces in placeconstraining the expansion of both economic activity and deforestation in the region.

Another robust result across all models for both dependent variables relates to transport costs.Transport costs to Sao Paulo, which is a proxy for access to national markets, have negative and

significant results in all models. This is in line with the theory showing that closer proximity to largemarkets is likely to contribute to economic activity and consequently in this case with deforestation aswell. However, we have the opposite result for transport costs to the nearest state capital. This is asurprising result and suggests that agriculture activities are developing faster in areas further away fromthe local urban centres, which raises the question about the role of regional markets. Local roads have

been the focus of much debate in the related literature. In our regressions once we control for other spatialvariables we do not find correlation between road length and deforestation. However, the variables aremarginally significant and positive in the output regression, indicating that they might be more importantfor economic growth than for the extensive use of land resources.

The cost variables also provide insights on the process of growth and deforestation. Both wagesand land prices are positive and significant in the output regression. As mentioned above wages serve as a

proxy for productivity it is significance is the in line with the basic microeconomic theory. Land prices inturn measures the stage of frontier development and higher growth where land is more expensive wouldadd to the arguments related o spatial economics and agglomeration effects, in particular in line with thevon Thunen basic proposition regarding the local of more productive farmers.

Looking at the spatial lags completes the characterization of the spatial dynamics. In the clearedland regression we find that output levels in neighbouring areas are negatively correlated and cleared landlevels in neighbouring areas are positively correlated with future expansion of land clearing. This tells usthat the expansion of the frontier is a complex process as we can see that deforestation is a spatial local

process and is likely to evolve from previous deforested areas but at the same time neighbours with largeeconomic activity contribute to prevent deforestation in their neighbours. Curiously spatial lags for

population are not significant in any model. In addition we see that the variables capturing global spatialautocorrelation are more important to explain output growth than the expansion of cleared land. In theoutput regression the spatial lags in the disturbances are highly significant and the spatial lag for thedependent variable is marginally significant. On the other hand, in the regression for cleared land only thespatial lag for the disturbance in the maximum likelihood model is significant, indicating that in this case

perhaps local spatial process dominate.A very interesting result comes from the human capital variable. Educational level is not

significant for output growth but is highly significant and negative for the expansion of cleared land. Wecan then speculate that formal educational does not contribute so much to how to work in agriculture ingeneral but somehow impacts how farmers are using land resources. Here further research is clearnecessary to unravel the relevant underlining mechanisms.

Our results do not support the literature on property rights as the proportion of farmers who ownsthe property where agricultural activities take place turned out to be insignificant in all models. Anotherresult on the agrarian structure relates to the relationship between farm size and economic performance.Here we find that the proportion of small properties has no correlation with subsequent output growth butat the same time higher proportion of small farms are positively correlated with larger land conversioncontrasting the arguments put forward by Fearnside (1993) and Walker et al (2000).

Finally we find that environmental conditions are relevant for land conversion but not so much foroutput growth. As expected soil quality is significant and has negative coefficient in the regression forcleared land. On the other hand the presence of the two types of forests (high forests and forests alongriver banks) are positively correlated with deforestation, which relates to the more stringent natural

constraints to extensive expansion of agriculture. The surprising result relates to the insignificance of thelevels of rain precipitation, which is counterintuitive and contrast with previous studies for the region(Chomitz and Thomaz 2001). We dont have good interpretation for this result but it is possible that our


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level of spatial aggregation is not appropriate to capture the local variation of rain levels. Interestinglyonly forests along river banks result to be significant in the regression for output. Again we dont have thetechnical information to interpret the result.

7. Conclusions

In this paper we have estimated four different models for growth of output and cleared land in theBrazilian Amazon. We extend the previous empirical literature in two ways. Firstly we motivate the study

by connecting the spatial processes of economic growth and deforestation with the modern literature onspatial economics and agglomeration. Secondly, we adopt spatial econometric methods that take intoaccount a wider range of spatial effects and control better for spatial autocorrelation.

The empirical results allow us to confront the factors impacting growth and deforestation and alsoto compare them with previous studies in the field. The main results provide evidence of the relevance ofspatial economics for understanding the reality in the Amazon region. Firstly, we find that agglomerationintensity has a non-linear relationship with both economic growth and deforestation, suggesting that at

initial levels of agglomeration positive externalities dominate and positively impact subsequent growth.However, negative externalities start to mount at higher levels of agglomeration imposing constraints togrowth of output and land clearing due to congestion effects. Moreover, spatial theory is supported by ourresults with respect to transport costs to national markets as proximity to Sao Paulo seems to be animportant factor for growth. However, the role of local roads is not duly evidenced by our estimations andwe join the authors who claim that the issue deserve much more careful analysis as the causalrelationships seem to be complex. The spatial autocorrelation in the cleared land regressions result to bemainly local than global, suggesting that the models in previous studies are not subject to significantmisspecification problems. However, in the output regressions global spatial autocorrelation is strongindicating that equations on economic growth must be extended accordingly.

Finally, we also provide evidence on the impact of other local characteristics such asenvironmental conditions, human capital and the agrarian structure. Here, some of the results are in linewith previous studies and others are not. We believe that the differences found indicate the complexity ofthe reality in the ground. Therefore, despite the rich existent literature in this field, more effortsenhancing the theoretical developments and applying more advanced empirical methods would certainlycontribute to enhance the understanding of underlining spatial processes of economic growth anddeforestation in the Brazilian Amazon.

References

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Appendix

Table 1a. Estimates for Change of Cleared Land

Variable OLS Spatial Lag Spatial Error GMM

Constant -7.783027 ***

(-3.113901)

-7.765370 ***

(-3.354698)

-7.826414 ***

(-3.426170)

-7.814971***

(-3.399360)

Product 0.231110 ***

(2.941851)

0.227151***

(3.099027)

0.243624 ***

(3.280600)

0.239927 ***

(3.250163)

Population 1.787846 ***

(3.499616)

1.797278 ***

(3.799263)

1.822853 ***

(3.882784)

1.812589 ***

(3.841859)

Population Sq -0.084978 ***

(-3.498842)

-0.085300 ***

(-3.792791)

-0.086492 ***

(-3.887154)

-0.086071 ***

(-3.845061)

Education -0.251633 **

(-2.385488)

-0.260124 ***

(-2.652770)

-0.254919 ***

(-2.707682)

-0.253734 ***

(-2.656228)Area 0.000003 **

(1.990535)

0.000004 **

(2.188212)

0.000004 **

(2.350194)

0.000004 **

(2.273938)

Clear -0.529640 ***

(-8.471051)

-0.528282 ***

(-9.098798)

-0.537877 ***

(-9.217546)

-0.535090 ***

(-9.182606)

Small Farms 0.958042 ***

(3.091515)

0.932464 ***

(3.244969)

0.896537 ***

(3.188464)

0.917436 ***

(3.234666)

Owners 0.352695

(1.188089)

0.345866

(1.258127)

0.330699

(1.226227)

0.338922

(1.245791)

Land Prices -0.000516

(-0.244821)

-0.000534

(-0.273619)

-0.000534

(-0.294263)

-0.000530

(-0.284639)

Wages 0.000041

(0.989404)

0.000044

(1.133143)

0.000051

(1.322853)

0.000047

(1.232385)

Herd Size 0.123786 ***

(3.700916)

0.122591 ***

(3.943987)

0.105967 ***

(3.568154)

0.111527 ***

(3.697749)

R2 0.4979 0.4961 0.5067 0.5024

*** significant at 99% confidence level

** significant at 95% confidence level* significant at 90% confidence level

t statistics in brackets

Additional control variables not reported: state dummies


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Table 1b. Estimates for Change of Cleared Land (Spatial Variables)


Transport Costs to

State Capital

0.000334 ***

(2.803578)

0.000333 ***

(3.025534)

0.000331 ***

(3.168911)

0.000332 ***

(3.117126)

Transport Costs to

Sao Paulo

-0.000264 ***

(-3.041796)

-0.000270 ***

(-3.348220)

-0.000281 ***

(-3.676980)

-0.000276 ***

(-3.547166)

Roads 0.000265

(1.352004)

0.000265

(1.462510)

0.000251

(1.387928)

0.000257

(1.417400)

Spatial Product -0.578938 ***

(-3.662931)

-0.562826 ***

(-3.824330)

-0.557981***

(-3.897923)

-0.564503 ***

(-3.913469)

Spatial Population -0.097340

(-0.827402)

-0.115129

(-1.045660)

-0.121690

(-1.168173)

-0.114173

(-1.080043)

Spatial Clearing 0.656950 ***

(7.276918)

0.655607 ***

(7.836074)

0.662139 ***

(7.965929)

0.660343 ***

(7.921477)Spatial Growth -0.065964

(-0.800723)

Spatial Error -0.203932 **

(-2.178038)

-0.137061

(-1.409423)

Table 1c. Estimates for Change of Cleared Land (Environmental Variables)


Rivers 0.000387(1.132157)

0.000373(1.175098)

0.000418(1.341049)

0.000409(1.304209)

Soil Quality -0.003875 **

(-2.059660)

-0.004024 **

(-2.300385)

-0.003976 ***

(-2.568506)

-0.003948 **

(-2.451574)

Rain 0.000092

(0.195775)

0.000026

(0.058937)

-0.000123

(-0.298787)

-0.000055

(-0.130373)

Temperature 0.002144

(0.148577)

0.004296

(0.320146)

0.009403

(0.738981)

0.007060

(0.545032)

Altitude -0.000061

(-0.173087)

-0.000049

( -0.149777)

-0.000076

(-0.251878)

-0.000070

(-0.226788)

Forests 1 0.006720 ***

(3.365466)

0.006888 ***

(3.704694)

0.006839 ***

(3.919555)

0.006811 ***

(3.822568)

Forests 2 0.015334 ***

(3.925459)

0.015514 ***

(4.275915)

0.014837 ***

(4.177826)

0.015021 ***

(4.197080)


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Table 2a. Estimates for Change in Output

Variables OLS Spatial Lag Spatial Error GMM

Constant -9.671001 ***

(-3.531708)

-9.863348 ***

(-3.912396)

-10.456095 ***

(-4.166140)

-10.150756 ***

(-4.016716)

Product -0.508285 ***

(-5.905632)

-0.525902 ***

(-6.633600)

-0.542531 ***

(-7.046461)

-0.528994 ***

(-6.779691)

Population 2.232378 ***

(3.988551)

2.287328 ***

(4.438959)

2.448331 ***

(4.840603)

2.363680 ***

(4.610341)

Population Sq -0.100965 ***

(-3.794393)

-0.103337 ***

(-4.218222)

-0.110321 ***

(-4.573883)

-0.106681 ***

(-4.367849)

Education -0.048360

(-0.418462)

-0.046173

(-0.433106)

-0.063369

(-0.584700)

-0.059143

(-0.545965)

Area 0.000003

(1.499950)

0.000003

(1.453897)

0.000002

(1.333567)

0.000002

(1.423744)Clear 0.092726

(1.353672)

0.099755

(1.580428)

0.090899

(1.469864)

0.092395 *

(1.477524)

Small Farms 0.041788

(0.123081)

0.094818

(0.302944)

0.087243

(0.277661)

0.071246

(0.225993)

Owners -0.118339

(-0.363862)

-0.135917

(-0.453924)

-0.214233

(-0.718060)

-0.176520

(-0.587105)

Land Prices 0.008265 ***

(3.582761)

0.007731 ***

(3.630501)

0.006877 ***

(3.017117)

0.007447 ***

(3.337400)

Wages 0.000102 **

(2.241791)

0.000103 **

(2.449727)

0.000102 **

(2.485971)

0.000102 **

(2.455834)

Herd Size 0.088753 ***

(2.422022)

0.092272 ***

(2.734862)

0.101726 ***

(2.906998)

0.096510 ***

(2.782937)

R2 Adjusted 0.2208 0.2144 0.2496 0.2347

*** significant at 99% confidence level

** significant at 95% confidence level

* significant at 90% confidence level

t statistics in bracketsAdditional control variables not reported: state dummies


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Table 2b. Estimates for Change in Output (Spatial Variables)


Transport Costs to

State Capital

0.000380 ***

(2.917518)

0.000386 ***

(3.210486)

0.000443 ***

(3.536359)

0.000420 * **

(3.384414)

Transport Costs to

Sao Paulo

-0.000241 ***

(-2.531374)

-0.000244 ***

(-2.784695)

-0.000273 ***

(-2.968466)

-0.000262 ***

(-2.883815)

Roads 0.000290

(1.350154)

0.000339 *

(1.716096)

0.000385 **

(1.981034)

0.000353 *

(1.794765)

Spatial Product -0.117498

(-0.678551)

-0.079695

(-0.494677)

-0.135484

(-0.821415)

-0.125933

(-0.772706)

Spatial Population -0.026677

(-0.206979)

-0.025672

(-0.216120)

0.017550

(0.138244)

-0.000575

(-0.004647)

Spatial Clearing 0.146885

(1.485084)

0.115676

(1.260884)

0.111467

(1.217002)

0.124331

(1.358296)Spatial Growth 0.143026 *

(1.755780)

Spatial Error 0.265015 ***

(3.500442)

0.163166 **

(1.968089)

Table 2c. Estimates for Change in Output (Environmental Variables)


Rivers 0.000255(0.680113)

0.000291(0.843726)

0.000281(0.816179)

0.000277(0.798117)

Soil Quality -0.003383

(-1.641255)

-0.003019

(-1.588251)

-0.002680

(-1.220671)

-0.002982

(-1.430985)

Rain 0.000218

(0.424156)

0.000242

(0.512046)

0.000138

(0.279715)

0.000165

(0.338249)

Temperature -0.000343

(-0.021706)

-0.001762

(-0.120794)

-0.000081

(-0.005318)

-0.000319

(-0.021244)

Altitude 0.000146

(0.377839)

0.000096

(0.268938)

0.000068

(0.174006)

0.000107

(0.282498)

Forests 1 0.001514

(0.692149)

0.001606

(0.797080)

0.002232

(1.051559)

0.001947

(0.930889)

Forests 2 0.016137 ***

(3.770760)

0.015610 ***

(3.957400)

0.015279 ***

(3.849405)

0.015615 ***

(3.927184)


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Table 3. Descriptive Statistics

Variables Mean Median Max Min St. Dev SkewnessEducational Index 1996 2.37135 2.29787 5.18449 0.55428 0.81121 0.563305Employment 1995 12729.7 6229 304523 216 26287.2 7.286956Land Prices 1985 993.136 602.168 32038.1 34.6218 2221.86 11.3555Wages 1995 165.611 54.8072 1976.43 0 267.746 2.985577

Permanent Agricultural Land (km2 1995) 3664.87 538.263 254334. 0 17801.0 11.60205Seasonal Agricultural Land (km2 1995) 18155.4 3636.55 147553 0 102495. 12.3752 Natural Pasture (km2 1995) 70543.1 10440.2 237479 0 211037. 7.380614Planted Pasture (km2 1995) 130655 15722.5 507364 0 500393. 7.975517 Natural Forests (km2 1995) 193313. 24335.7 770377 1.716 792465 7.014679Planted Forests (km2 1995) 1345.81 4.08 75937 0 7473.87 7.680762Herd Size 1995 140252 25714 4857335 0 493703 7.335426Paved Roads 1991 52.0155 0 1412.88 0 153.583 5.978845 Non-Paved Roads 1991 117.055 0 4965.49 0 442.719 7.52083Transport Costs to So Paulo 1995 3379.75 2951.62 10511.9 1270.5 1647.13 2.413455Transport Costs to State Capital 1995 960.324 758.341 5949.00 0 960.914 3.285705Farm Size 10 100 1000 100000 ha (share) 0.00852 0 0.92726 0 0.07053 10.99526Owners (share) 0.85571 0.94450 1 0.05591 0.20636 -2.22641Renters (share) 0.01697 0.00272 0.36573 0 0.03774 4.933829Sharecroppers (share) 0.00637 0.00065 0.13845 0 0.01764 4.971965Squatters (share) 0.12094 0.03969 0.91727 0 0.19867 2.537895Rivers (km) 54.9218 0 2282.74 0 181.197 8.163904Rain 610.194 593.108 1016.57 0 181.054 -0.87523

Good Soil (share) 8.19131 0 100 0 21.1202 3.151817Temperature in June 24.7090 25.7890 27.3602 0 4.70338 -4.60976Temperature in September 26.4977 27.2798 29.3388 0 4.91168 -4.93647Temperature in December 26.0486 26.9873 29.3833 0 4.84036 -4.89093High Forests (%) 21.4401 0 97.7340 0 32.2902 1.085811River Forests (%) 7.25843 0 97.221 0 15.0501 3.021361SemidecidualForests (%) 1.52949 0 60.2253 0 7.06142 5.788492Short Forests (%) 31.3512 15.9987 100 0 34.5750 0.855985Shrubs (%) 25.8387 6.43768 100 0 33.2936 1.031956 Natural Fields (%) 7.06753 0.1478 91.8460 0 14.8838 2.906489MCA Area 1997 19748.8 3542.4 361329 104.8 49952.0 4.622097Altitude 129.691 60 1186 0 153.943 2.448508

Agricultural Output 1995 22.2085 7 0 7.245671 932.04 .207216 3.15438 9.331964

Deforestation, Growth and Agglomeration Effect

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